Assessing Urban Land Parcel Dynamics Driven by Bus Rapid Transit (BRT) as an Exclusive Transit Route

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript examines how exclusive transit routes, specifically Bus Rapid Transit (BRT) systems, influence urban land parcel dynamics. By employing the C5.0 decision tree algorithm, the study identifies key predictors of land use changes, such as property condition, value, and proximity to transit stations. The manuscript can be improved after some revisions.

1. While the manuscript identifies factors influencing land use, it lacks an in-depth explanation of the causal relationships between these variables. For example, the impact of feeder routes is mentioned but not fully explored.

2. The case study in Lahore is highly localized. To strengthen generalizability, you should include comparative data or a discussion of how these findings might apply in other cities with different urban dynamics.

3. Although the paper mentions multicollinearity issues with parametric models, there is limited discussion on how the C5.0 decision tree algorithm effectively overcomes this challenge.

4. Figures and tables are not properly labeled, making it difficult for readers to connect them with the corresponding analysis.

5. The resolution of the figure on page 21 is too low.

 

6. On page 2, line 58, there is a typographical error in "studded much" which should be "studied much." Additionally, avoid redundant phrasing like "empirical validation, expert input and recurring technique adopted for enlisting variables".

Author Response

• While the manuscript identifies factors influencing land use, it lacks an in-depth explanation of the causal relationships between these variables. For example, the impact of feeder routes is mentioned but not fully explored.

Reply: Thank you for this valuable feedback. We recognize the importance of elaborating on the causal relationships between the identified variables, such as the impact of feeder routes. In response, we have expanded our discussion to provide a more detailed explanation. The new text is added in manuscript.

Research has shown that feeder routes play a crucial role in amplifying these impacts, as they extend transit accessibility beyond primary corridors, indirectly increasing land values and spurring shifts in land use within the larger catchment area (Bocarejo et al., 2012; Duarte & Rojas, 2018).

(Please see page # 3, lines 137-140 of manuscript)

• The case study in Lahore is highly localized. To strengthen generalizability, you should include comparative data or a discussion of how these findings might apply in other cities with different urban dynamics.

Reply:  Thank you for this valuable feedback. In response, I would like to highlight that existing studies on transit impacts commonly utilize parametric approaches, which rely on fixed assumptions and may struggle with multicollinearity, thus limiting their generalizability. This study, however, employs the non-parametric C5.0 decision tree model, which is inherently adaptable and handles multicollinearity effectively. The non-parametric nature of C5.0 allows it to reveal complex relationships without relying on strict assumptions, making the results more generalizable. Moreover, if additional variables are introduced, this model can integrate those seamlessly, recalibrating to identify new relationships without compromising the robustness of existing findings. This adaptability makes the C5.0 model a more versatile and broadly applicable tool for understanding transit impacts across varying urban contexts. The new text to explain have been added in manuscript.

Non-parametric models like C5.0 offer flexibility across diverse datasets, enabling new variables to be added without redefining the model structure—a key advantage for urban studies in varying city contexts (Golub, Guhathakurta, & Sollapuram, 2012).

(Please see page # 4, lines 191-194 of manuscript)

The relationship between various land and transit characteristics, particularly concerning proximity and accessibility, is effectively captured by the C5.0 model in this study. By incorporating data from similar variables in other case studies, these results can be validated across different urban contexts. Furthermore, if specific characteristics of a particular city necessitate the inclusion of additional variables, the model can be adapted to integrate these unique features, thereby enhancing the applicability of the findings.

(Please see page # 36-37, lines 1219-1226 of manuscript)

• Although the paper mentions multicollinearity issues with parametric models, there is limited discussion on how the C5.0 decision tree algorithm effectively overcomes this challenge.

Reply:  Thank you for this valuable feedback. In response to suggestion; the new text to explain have been added in manuscript.

“The C5.0 decision tree algorithm is particularly effective in addressing multicollinearity, as it does not rely on assumptions of linearity or independence among variables, unlike parametric models. Instead, C5.0 sequentially selects features based on their contribu-tion to reducing uncertainty, thereby naturally handling highly correlated variables without affecting model performance. By prioritizing variables with the most predictive power, C5.0 can differentiate the impact of closely related factors—such as proximity and accessibility in this study—without distorting results, making it a robust choice for complex urban transit analyses.”

(Please see page # 15, lines 532-539 of manuscript)

• Figures and tables are not properly labeled, making it difficult for readers to connect them with the corresponding analysis.

Reply:  Thank you for this valuable feedback. In response to suggestion; the figures and tables are now properly labelled and references of these labels in manuscript have been added at required places.

• The resolution of the figure on page 21 is too low.

Reply: Thank you for this valuable feedback. In response to suggestion; the C5.0 model have been re-drawn at figure # 12 and its details have been prepared in tabular for in table # 13. 

(Please see page # 24-28, lines 826-831 of manuscript)

• On page 2, line 58, there is a typographical error in "studded much" which should be "studied much." Additionally, avoid redundant phrasing like "empirical validation, expert input and recurring technique adopted for enlisting variables".

Reply: Thank you for this valuable feedback. In response to suggestion; the mistake have been corrected.

“studied much.”

(Please see page # 2, line 64 of manuscript)

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

 

Dear authors,

you did a very interesting study, but I have the following comments:

Data collection

 You present data from 4 distinct years 2005,2010,2015 and 2018, why not data from recent years? Is there any change during the Covid or post covid era? I see the basic problem of the study in the analysis of the values ​​for COVID 19, from the article I cannot judge whether these data can be confirmed in the post-COVID era, or whether the situation has changed. It will be necessary for the authors to take a position on this in the Discussion chapter.

 

If this paper is part of the work of the still ongoing PHD work, I would recommend adding current data including the data from and after COVID.

 

Literature review:

I recommend adding to the literature review more sources that focus on the non-parametric approach.

There are also new studies analyzing the impact of BRT/public transport on land values. The Alonso study from 1964 is very old.

You wrote about the well-maintained and poorly-maintained parcels. My question is, were such results to be expected? Adding a basic explanation of the well-maintained and poorly-maintained parcels. Basically, more developed parcels would be always more attractive.

In the study, you state that one of the key attributes was the provision of parking. My question, however, is whether it is a consequence of the BRT system or the combination of Park and Ride, or is it instead a consequence of the fact that there are shops at the given station where people go shopping. Here it is necessary to distinguish that these customers are representing the car users who use the possibility that there are shops at the BRT stations, but they themselves do not use the BRT system. So this can be another key aspect which you could mention in discussion. How the BRT provision is affecting the car users or not transit users.

Other comments:

 

I am a bit surprised that authors are using non institutional emails. Instead they presents the email from google, yahoo services what seems a bit nonprofessioanl.

 

Figure - Decision tree 5.0 is too small, therefore, I recommend leaving it out of the study

 

I strongly recommend editing the manuscript according to the journal template and guideline. Especially formatting, citations, labeling of figures and tables. Currently, it is more of a draft version that cannot be published.

 

Try to use the same size of font in the Figures. Sometimes you have the very small font, sometime big. Try to unify them. e.g. figure Transit route factors and word Accelerator should  be in one line (line 216).

Author Response

• You present data from 4 distinct years 2005,2010,2015 and 2018, why not data from recent years? Is there any change during the Covid or post covid era? I see the basic problem of the study in the analysis of the values ​​for COVID 19, from the article I cannot judge whether these data can be confirmed in the post-COVID era, or whether the situation has changed. It will be necessary for the authors to take a position on this in the Discussion chapter.

If this paper is part of the work of the still ongoing PHD work, I would recommend adding current data including the data from and after COVID.

Reply:  Thank you for this insightful comment. The study purposefully excluded COVID-19 and post-COVID data to focus on capturing long-term trends in land parcel transformations that are directly influenced by the Bus Rapid Transit (BRT) system, without interference from pandemic-driven anomalies. The COVID-19 era introduced atypical fluctuations in land values and travel patterns, influenced by factors like restricted mobility, economic slowdowns, and shifting urban dynamics. Including data from this period could distort the assessment of BRT’s impacts, as the changes would likely reflect pandemic-related disruptions rather than consistent, transit-driven development trends. However, we acknowledge the importance of understanding the potential long-term impacts of COVID-19 on urban development. Future studies that integrate post-COVID data could provide additional insights into how urban transit systems influence land values and usage patterns under these new dynamics. The new text to explain this have been added in manuscript.

“Recent studies indicate that the COVID-19 pandemic temporarily disrupted land values and travel behavior, complicating assessments of long-term BRT impacts on land variables. Yan et al. (2024) found that COVID-19’s effects on property prices near Wuhan metro stations resulted in decreased land values, particularly around residential and employment-centered stations. Their analysis highlights that transit-adjacent properties experienced reduced demand due to perceived health risks and limited commuting during lockdowns. Similarly, Gabrielli et al. (2024) analyzed global real estate trends, noting significant declines in high-density property demand due to pandemic-induced economic uncertainty and social distancing measures. These atypical fluctuations underscore how the pandemic skewed property values, making it challenging to include this period in general assessments of BRT impacts on land use, value, and density. Excluding pandemic-era data offers a clearer picture of transit system effects in standard economic conditions, aiding in reliable evaluation of long-term transit-driven urban development.”

(Please see page # 6, lines 232-244 of manuscript)

“The COVID-19 pandemic period was excluded from analysis to ensure data reflect normal economic conditions, as pandemic-driven disruptions in land value and travel patterns could distort the study's evaluation of BRT impacts on land variables.”

(Please see page # 6, lines 263-265 of manuscript)

“It is essential to note that COVID-19 significantly impacted land values and travel behavior, leading to atypical patterns that could obscure the long-term effects of the BRT system on urban land dynamics. By focusing on pre- and post-BRT data outside the pandemic period, the study maintains a clearer view of BRT-driven influences on land use, value, and other variables of study.”

(Please see page # 34, lines 1089-1093 of manuscript)

Literature review:

• I recommend adding to the literature review more sources that focus on the non-parametric approach.

Reply:  Thank you for this valuable feedback. The new sources to explain this have been added in manuscript.

“Recent advancements in non-parametric methods, particularly decision tree algo-rithms like C5.0, have enhanced urban transit research by effectively managing complex datasets. These approaches mitigate multicollinearity and reveal nuanced variable in-teractions that parametric methods often overlook. Studies by Wang et al. (2020) and Yang et al. (2021) highlight C5.0’s effectiveness in capturing detailed patterns in urban land use, while Saadi et al. (2023) further demonstrates its adaptability to trans-it-induced urban growth, underscoring C5.0's utility in transit-based analysis where traditional methods fall short.”

(Please see page # 4, lines 179-186 of manuscript)

• There are also new studies analyzing the impact of BRT/public transport on land values. The Alonso study from 1964 is very old.

 

Reply:  Thank you for this valuable feedback. The new studies to explain this have been added in manuscript.

“Cervero & Dai (2014) provide a foundation for understanding TOD impacts but lack specificity in how TOD principles apply to varying urban forms and densities, potentially limiting the theory’s applicability across diverse regions. Theories like Agglomeration Economies Theory (Graham, 2007), Accessibility Theory (Geurs & van Wee, 2004), and Sustainable Accessibility Theory (Bertolini, Le Clercq & Kapoen, 2005) expand upon traditional frameworks by highlighting how transit accessibility encourages economic clustering, which in turn raises land value in high-access areas. Accessibility Theory further suggests that enhanced connectivity near transit hubs boosts development potential by reducing travel times, while Sustainable Accessibility Theory emphasizes the integration of transportation with sustainable land use planning. Together, these perspectives provide a more nuanced understanding of transit's multifaceted impact on urban land values, illustrating how accessibility, economic clustering, and sustainable planning jointly influence demand for land near transit corridors. “

(Please see page # 4-5, lines 200-212 of manuscript)

• You wrote about the well-maintained and poorly-maintained parcels. My question is, were such results to be expected? Adding a basic explanation of the well-maintained and poorly-maintained parcels. Basically, more developed parcels would be always more attractive.

Reply:  Thank you for your question. Yes, as a consequence of the BRT construction, we observed a transformation in parcel conditions: poorly-maintained parcels were often upgraded to well-maintained status, while well-maintained parcels were further converted to more attractive and commercially viable uses. This shift aligns with typical patterns of transit-induced urban development, where transit accessibility boosts investment incentives. The BRT system enhanced connectivity and economic potential in the area, driving improvements in parcel maintenance and encouraging commercial development for parcels already in good condition. This trend underscores the BRT’s role in stimulating urban revitalization and optimizing land use. The new text has been added in manuscript.

“The categorization of building conditions in this study aligns with international standards set by the Royal Institution of Chartered Surveyors (RICS) and the Chartered Institute of Public Finance and Accountancy (CIPFA) in the UK, which offer a structured framework for assessing building quality and physical state. Two additional categories were added to capture specific conditions relevant to the study area: Vacant plots, indicating undeveloped parcels, and Excellent Condition buildings, reflecting newly constructed structures developed as urban renewal projects after demolishing old buildings and altering business uses in response to BRT implementation. This adjustment enables a more nuanced assessment that aligns with local urban development patterns and the transformative effects of transit infrastructure.”

(Please see page # 11, lines 381-390 of manuscript)

Sr. No.	Land parcel condition	Description (2.4)	Label Value
1	Vacant / No Building	Indicates undeveloped parcels with no structures.	1
2	Old and poor condition	The building is in unacceptable condition, potentially posing safety risks due to severe deterioration and system failures.	2
3	Bad condition building	The building shows significant deterioration with several noticeable defects, which may affect functionality and requiring major repairs.	3
4	Normal condition	The building has some minor defects or signs of wear but remains functional overall.	4
5	Good condition building	The building is in optimal condition, showing minimal signs of wear with all systems fully operational.	5
6	Excellent condition	Newly constructed structures developed as urban renewal projects after demolishing old buildings and altering business uses, showing high-quality finishes and modern standards.	6

 

(Please see page # 11, line 395 of manuscript)

• In the study, you state that one of the key attributes was the provision of parking. My question, however, is whether it is a consequence of the BRT system or the combination of Park and Ride, or is it instead a consequence of the fact that there are shops at the given station where people go shopping. Here it is necessary to distinguish that these customers are representing the car users who use the possibility that there are shops at the BRT stations, but they themselves do not use the BRT system. So this can be another key aspect which you could mention in discussion. How the BRT provision is affecting the car users or not transit users.

Reply:  Thank you for your comment and observation. In this study, we focus specifically on the BRT Lahore stations that were constructed with varying designs in terms of parking facilities. Some stations included dedicated Park-and-Ride facilities intended for BRT commuters, while others were built without parking options. Our analysis considers only these purpose-built parking facilities as part of the BRT infrastructure and does not encompass adjoining commercial area parking.

The study, therefore, solely examines the impact of parking provision as it relates to BRT user accessibility and convenience, rather than parking used by car users for commercial activities nearby. This distinction helps isolate the influence of BRT-related parking on land parcel changes, ensuring our findings are relevant to the intended purpose of the Park-and-Ride facilities associated with the BRT system.

To clarify this text have been added / modified in the manuscript.

Parking, which refers to the availability of park and ride facility at BRT stations;

(Please see page # 13, lines 422-423 of manuscript)

Description	Variables
Availability of Park and ride facility at BRT stations	Parking Provision

(Please see page # 13, lines 457 of manuscript)

Other comments:

• I am a bit surprised that authors are using non institutional emails. Instead they presents the email from google, yahoo services what seems a bit nonprofessional.

Reply:  Thank you for your observation regarding the use of non-institutional email addresses. As a PhD student at UTM, I am set to complete my studies in December 2024, after which my university email ID will no longer be active. Similarly, Author 5, also from UTM, expects to complete their PhD by March 2025. Author 3, a former Senior Lecturer and my PhD supervisor at UTM, has transitioned to a role in municipal government in the USA, resulting in a change in his academic affiliation and email. Finally, Author 4, who recently completed his PhD at KU Leuven, Belgium, will also be undergoing an email change as he transitions from his academic role. Given these changes, we opted to use more stable, non-institutional email addresses to ensure continuity and accessibility for future correspondence. In response to your comment; the institutional email of author 5 have been added.

Chuhdary@graduate.utm.my 

(Please see page # 1, line 7 of manuscript)

• Figure - Decision tree 5.0 is too small, therefore, I recommend leaving it out of the study

Reply: Thank you for this valuable feedback. In response to your valuable comment; the C5.0 model have been re-drawn at figure # 12 and its details have been prepared in tabular form in table # 13. 

(Please see page # 24-28, lines 826-831 of manuscript)

• I strongly recommend editing the manuscript according to the journal template and guideline. Especially formatting, citations, labeling of figures and tables. Currently, it is more of a draft version that cannot be published.

Reply:  Thank you for this valuable feedback. In response to suggestion; the figures and tables are now properly labelled and references of these labels in manuscript have been added at required places.

• Try to use the same size of font in the Figures. Sometimes you have the very small font, sometime big. Try to unify them. e.g. figure Transit route factors and word Accelerator should be in one line (line 216).

Reply:  Thank you for your feedback on the consistency of font sizes in the figures. We have reviewed and standardized the font sizes across all figures to ensure a cohesive presentation. Additionally, we adjusted the layout in the "Transit route factors" figure to ensure that "Accelerator" and other labels align correctly on a single line. The updated figures now maintain a uniform appearance for improved readability.

(Please see page # 7, line 278 of manuscript)

(Please see page # 8, line 323 of manuscript)

(Please see page # 9, line 331 of manuscript)

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Journal: Urban Science (ISSN 2413-8851)

Manuscript ID: urbansci-3276533

Type: Article

Assessing Urban Land Parcel Dynamics Driven by Exclusive Transit Routes by Rana Tahir Mehmood, Prof. Ts. Dr. Muhammad Zaly Shah Bin Muhammad Hussein, Dr. Mehdi Moeinaddini, Dr. Muhammad Mashhood Arif, Ramine Chahdury

 

The manuscript is about researching how the addition of transit routes affects urban development, particularly on nearby parcels of land. The author uses a non-parametric model to analyze the relationship between parcel characteristics and transit routes, identifying key factors that influence this dynamic.

 

The title is incomplete.

Poorly organized affiliations. Read the instructions of Urban science, or look at already published articles

You do not have an institution for the author no.5. Provide institutional emails

 

This introduction lays the foundation for further research. it is well structured. Discusses rapid urbanization and the need for sustainable transit solutions, with particular emphasis on BRT systems. It describes BRT's flexibility, cost-effectiveness, and capacity for rapid passenger transportation, highlighting the complexity of the relationship between the BRT system and urban development, citing the various factors that influence this dynamic. Then the authors cite the literature, where there are shortcomings in the analysis, which leads the authors of this paper to develop a new approach. The authors state that they will conduct research with non-parametric techniques for analysis.

 

It would be useful to add a brief definition of the BRT system in the introduction, to clarify the importance of BRT to the reader

Write a summary of the work at the end of the introduction. Emphasize how this analysis can contribute to the development of theory and practice

 

A drawback of the literature review is the lack of recent research and references. Add in both overview and references

 

It is explained how parametric methods have their limitations, and non-parametric techniques are discussed. It would be good to give a brief example of how these methods have been used in other studies.

 

Correct abbreviations throughout articles. You have abbreviations that you enter several times. You have abbreviations that you never write down what they mean. You have shortcuts that you introduce and then don't use. Arrange carefully

 

Make numbering of formulas, figures and tables, according to the requirements of the article. Write the description of the figures and tables in the caption. Create calls for formulas, figures and tables throughout the text.

 

In the methodology on key variables, it would be useful to add a brief explanation of why exactly these variables were chosen, and to confirm their relevance to the research.

 

 Consider adding a sentence that connects the identification of variables to the data collection process, emphasizing how those variables guided the research.

You mention that the research methodology is visually represented in the figure. A brief description of the content of the figure could improve understanding.

 

Explain how the quantitative design specifically helps answer the research questions or objectives. Also, you could further explain the benchmarking and how it complements the decision model.

 

Define the term "cross-validation techniques".

When you mention using the C5.0 model, it would be helpful to add a brief explanation of how that model works and what specific aspects it analyzes.

 

In the research design section, you can add a sentence that explains how the results of the analysis will be presented or interpreted.

 

In the data analysis section, consider breaking long sentences into shorter, clearer sentences. In some parts you use different expressions for the same concepts. It would be a good idea to pick one term and use it consistently.

 

when building the model, add more explanation for the steps used in the C5.0 algorithm

 

Correct the form of the tables according to MDPI requirements. The letters in the tables should be smaller than in the text

 

The first figure between lines 176 and 177 should be clearly visible, so that the letters are in the size of the text. The text in the second image between lines 176 and 177 is completely invisible. Correct it

 

Page 21 is very bad. It can't be like that in an article. Sort it out

 

 

The research is quite thorough and uses an interesting approach to analysis. Something that could be considered is additional research into community impacts, such as social and economic dynamics, given the changes brought about by the new routes. Also, consideration of potential negative effects, such as increased pollution, would further enrich the analysis.

Author Response

• The title is incomplete.

 

Reply:  Thank you for pointing that out. In response to your feedback, we have updated the title from "Assessing Urban Land Parcel Dynamics Driven by Exclusive Transit Routes" to "Assessing Urban Land Parcel Dynamics Driven by Bus Rapid Transit (BRT) as an Exclusive Transit Route" to provide clarity and specificity regarding the focus on BRT systems. This revised title now better reflects the study's core objectives and content.

(Please see page # 1, line 2-3 of manuscript)

 

• Poorly organized affiliations. Read the instructions of Urban science, or look at already published articles

Reply:  Thank you for the feedback. We have reorganized the affiliations according to the formatting guidelines provided by Urban Science, ensuring consistency with the journal's standards as observed in previously published articles. The necessary adjustments have been made to improve clarity and professionalism in the author information section.

(Please see page # 1, line 6-10 of manuscript)

 

• You do not have an institution for the author no.5. Provide institutional emails

 

Reply:  Thank you for pointing this out. We have updated the author information for Author 5 to include their institutional affiliation and provided an institutional email as per the journal's requirements.

(Please see page # 1, line 6-7 of manuscript)

• This introduction lays the foundation for further research. it is well structured. Discusses rapid urbanization and the need for sustainable transit solutions, with particular emphasis on BRT systems. It describes BRT's flexibility, cost-effectiveness, and capacity for rapid passenger transportation, highlighting the complexity of the relationship between the BRT system and urban development, citing the various factors that influence this dynamic. Then the authors cite the literature, where there are shortcomings in the analysis, which leads the authors of this paper to develop a new approach. The authors state that they will conduct research with non-parametric techniques for analysis.

Reply:  Thank you for your thorough review and positive feedback on the structure and clarity of our introduction. We aimed to clearly convey the motivation behind our research by highlighting the limitations of existing parametric approaches in BRT impact studies and emphasizing the potential of non-parametric methods to address these gaps. By applying the C5.0 decision tree model, we hope to offer a novel perspective that captures the complex, non-linear relationships between transit infrastructure and urban development dynamics. Your comments affirm the importance of this approach in advancing the field. Furthermore, in response some clarification regarding use of non-parametric analysis approach is added in literature review part of the manuscript.

 

“Cervero & Kang (2011) and Rodríguez & Mojica (2009) primarily focus on proximity to stations as a factor driving land use intensity but fall short in examining the combined influence of other elements, such as station amenities or surrounding infrastructure, on urban development”.

(Please see page # 3, line 133-137 of manuscript)

Atkinson-Palombo (2010) highlights that multicollinearity issues can obscure the specific contributions of individual transit factors, while Bhattacharjee & Goetz (2016) stress the difficulty of isolating unique impacts in highly interconnected urban systems.

(Please see page # 4, line 158-161 of manuscript)

 

Dubé, Thériault & Des Rosiers (2013) noted that their findings were limited by an inability to fully account for overlapping impacts of accessibility and land use in high-density areas, suggesting a need for alternative methods.

(Please see page # 4, line 164-166 of manuscript)

Despite the promising findings, Golub, Guhathakurta & Sollapuram (2012) did not extend their analysis to other transit modes or geographical contexts, which limits the generalizability of their results.

(Please see page # 4, line 173-175 of manuscript)

             However, Wang et al. (2020) and Yang et al. (2021) did not specifically address BRT systems, limiting insights into how C5.0 could be tailored to the unique dynamics of BRT-induced land development. Saadi et al. (2023) focused on general urban growth patterns without isolating the specific impacts of transit features like feeder routes or proximity to BRT stations, leaving a gap in the applicability of their approach to BRT contexts.

(Please see page # 4, line 186-191 of manuscript)

Cervero & Dai (2014) provide a foundation for understanding TOD impacts but lack specificity in how TOD principles apply to varying urban forms and densities, potentially limiting the theory’s applicability across diverse regions.

(Please see page # 4, line 200-202 of manuscript)

Despite these theoretical contributions, Graham (2007), Geurs & van Wee (2004), and Bertolini, Le Clercq & Kapoen (2005) do not address how transit infrastructure characteristics variations might mediate the relationship between accessibility and land value, highlighting a need for empirical studies that incorporate such factors.

(Please see page # 5, line 215-219 of manuscript)

Rodríguez & Mojica (2009), Guerra & Cervero (2011), and Deng & Nelson (2013) primarily focus on high-density urban settings and may not account for variations in BRT impacts in less densely populated or economically different regions.

(Please see page # 5, line 221-224 of manuscript)

This limited focus restricts current understanding and poses challenges in generalizing findings to diverse urban environments

(Please see page # 5, line 226-228 of manuscript)

• It would be useful to add a brief definition of the BRT system in the introduction, to clarify the importance of BRT to the reader

Reply:  Thank you for the suggestion. A brief definition of the Bus Rapid Transit (BRT) system has been added to the introduction to provide readers with a clearer understanding of its significance. This addition clarifies BRT’s role as a high-capacity, cost-effective transit solution that operates on exclusive lanes, improving efficiency and accessibility in urban areas. This foundational context enhances the reader's comprehension of BRT’s importance in shaping urban development.

“Bus Rapid Transit (BRT) systems are high-capacity, efficient public transport solutions that provide metro-level service quality with the flexibility and lower costs of bus in-frastructure. BRT operates on exclusive lanes separated from general traffic, ensuring fast and reliable travel. Key elements include dedicated lanes, off-board fare collection, intersection priority, and platform-level boarding, all of which enhance efficiency and the passenger experience (Levinson et al., 2003; ITDP, 2016).”

(Please see page # 1, line 33-39 of manuscript)

• Write a summary of the work at the end of the introduction. Emphasize how this analysis can contribute to the development of theory and practice

Reply:  Thank you for the suggestion. A brief summery have been added at the end of introduction part.

This study investigates the influence of Bus Rapid Transit (BRT) systems on urban land development, focusing on their ability to support dense, mixed-use growth. Tra-ditional parametric models struggle with multicollinearity when analyzing these ef-fects. To overcome this, the research employs the C5.0 decision tree model, a non-parametric approach that adeptly captures complex, non-linear interactions be-tween transit features with land parcels and their strength variation by proximity and accessibility within catchment area. The findings offer fresh insights into BRT’s capacity to foster sustainable urban development.

(Please see page # 3, line 114-121 of manuscript)

 

• A drawback of the literature review is the lack of recent research and references. Add in both overview and references

Reply:  Thank you for the feedback. Recent studies and updated references have been added to the literature review to address this concern. This addition strengthens the review by incorporating the latest findings and advances in BRT and urban development research, ensuring a more comprehensive and current overview.

Recent studies, such as those by Kim et al. (2022) and Jiang et al. (2023), have further substantiated these findings, highlighting the role of BRT in driving both residential and commercial development patterns in urban corridors, especially in emerging economies.

(Please see page # 3, line 130-133 of manuscript)

Recent studies by Lin et al. (2023) and Raj et al. (2022) have pointed out that these multicollinearity issues obscure the effects of specific transit-related features, underscoring the need for more robust, non-parametric models.

(Please see page # 4, line 156-158 of manuscript)

Studies by Mor et al. (2023) and Xie et al. (2022) affirm that TOD principles significantly promote development in cities with emerging transit infrastructure, underscoring the importance of these theoretical models.

(Please see page # 5, line 212-215 of manuscript)

• It is explained how parametric methods have their limitations, and non-parametric techniques are discussed. It would be good to give a brief example of how these methods have been used in other studies.

Reply:  Thank you for the suggestion. New examples have been added to illustrate the application of non-parametric methods in other studies. Specifically, Wang et al. (2020) and Yang et al. (2021) employed non-parametric techniques, though they did not specifically target BRT systems, which limits the direct relevance of their findings for BRT-induced land development. Additionally, Saadi et al. (2023) investigated general urban growth patterns, but without isolating transit-specific factors like feeder routes or proximity to BRT stations, indicating a gap in adapting such methods to BRT contexts. This study aims to bridge this gap by tailoring the C5.0 model to assess BRT-related urban dynamics.

Despite the promising findings, Golub, Guhathakurta & Sollapuram (2012) did not ex-tend their analysis to other transit modes or geographical contexts, which limits the generalizability of their results.

(Please see page # 4, line 173-175 of manuscript)

However, Wang et al. (2020) and Yang et al. (2021) did not specifically address BRT systems, limiting insights into how C5.0 could be tailored to the unique dynamics of BRT-induced land development. Saadi et al. (2023) focused on general urban growth patterns without isolating the specific impacts of transit features like feeder routes or proximity to BRT stations, leaving a gap in the applicability of their approach to BRT contexts.

(Please see page # 4, line 186-191 of manuscript)

• Correct abbreviations throughout articles. You have abbreviations that you enter several times. You have abbreviations that you never write down what they mean. You have shortcuts that you introduce and then don't use. Arrange carefully

Reply:  Thank you for highlighting this issue. We have reviewed and standardized abbreviations across the article. All abbreviations are now defined upon first use, and now this revision ensures consistency and clarity, enhancing the overall readability of the manuscript.

Transit-Oriented Development (TOD)

(Please see page # 3, line 141 of manuscript)

Bus Rapid Transit (BRT) (3.9)

(Please see page # 3, line 144 of manuscript)

• Make numbering of formulas, figures and tables, according to the requirements of the article. Write the description of the figures and tables in the caption. Create calls for formulas, figures and tables throughout the text.

Reply:  Thank you for this valuable feedback. In response to suggestion; the figures and tables are now properly labelled and references of these labels in manuscript have been added at required places.  The numbering of formulas has also been added in the manuscript. The detailed description of the figures as suggested has also been added in manuscript.

 

• In the methodology on key variables, it would be useful to add a brief explanation of why exactly these variables were chosen, and to confirm their relevance to the research.

 

Reply:  Thank you for the suggestion. We have now added a brief explanation in the methodology section, clarifying why each key variable was selected based on its relevance to BRT-induced urban development. These variables—such as land value, population density, and building condition—were chosen due to their direct impact on land parcel transformation and their frequent use in prior studies on transit effects. This selection was also validated through empirical evidence and expert consultation, confirming their significance in analyzing the relationship between BRT systems and urban land changes.

 

“A comprehensive review initially highlighted relevant variables, such as land value and population density (Rodriguez & Allen, 2022; Kamruzzaman et al., 2014). Similar ap-proaches were used in studies by Golub et al. (2012) and Delmelle & Duncan (2013), which employed empirical data and expert input to refine transit-related variables. Reconnaissance data from Lahore Metro Bus System, spanning pre- and post-BRT pe-riods, validated the relevance of these variables, showing shifts due to BRT proximity. Consultations with urban planners and transit officials further refined these variables to reflect BRT-driven development impacts accurately. Comparable methods have been utilized by Páez & Scott (2005) and Miller & Wentz (2003) to select variables in urban transit studies.”

(Please see page # 6, lines 247-256 of manuscript)

• Consider adding a sentence that connects the identification of variables to the data collection process, emphasizing how those variables guided the research.

 

Reply:  Thank you for this suggestion. A sentence has been added to clarify that the identification of key variables directly informed the data collection process, ensuring targeted and relevant data gathering for each variable. This approach allowed us to focus on variables like land value, accessibility, and population density, guiding our data collection to accurately capture the BRT's impact on urban land dynamics.

“These identified variables directly guided data collection, ensuring alignment with the study's objectives and allowing precise analysis of BRT proximity impacts on land de-velopment.”

(Please see page # 6, lines 258-260 of manuscript)

• You mention that the research methodology is visually represented in the figure. A brief description of the content of the figure could improve understanding.

Reply:  Thank you for this feedback. A brief description has been added after research design figure to enhance clarity, outlining that the figure provides a visual summary of the research methodology, including the stages of variable identification, data collection, comparative analysis, and application of the C5.0 model. This description will aid readers in understanding how each methodological component aligns with the study's objectives.

“The figure provides an overview of the research methodology, showing the process of identifying and mapping key variables for analysis. It begins with the selection of Land Parcel Change Variables and Transit Route Factors, followed by GIS-based map-ping to capture Accessibility and Proximity data. This information supports data prep-aration and processing, with Dependent and Independent Variables defined for the C5.0 model application. Finally, results are integrated after boosting and pruning to refine insights into the impact of BRT on urban land parcels.”

(Please see page # 7, lines 282-288 of manuscript)

• Explain how the quantitative design specifically helps answer the research questions or objectives. Also, you could further explain the benchmarking and how it complements the decision model.

Reply:  The quantitative design is fundamental to addressing the research questions, as it provides a structured approach to measuring and analyzing the effects of BRT-related variables on land parcel changes. By quantifying changes in land value, land use, population density, and building conditions, this design enables a precise assessment of the impact of BRT. Benchmarking serves as a comparative standard, allowing for an evaluation of pre- and post-BRT conditions. This complements by establishing baseline conditions against which post-BRT impacts are assessed, thereby enhancing the model’s explanation and accuracy in predicting the influence of BRT on urban land dynamics.

The quantitative design of the study was essential in addressing the research questions, as it enabled the identification and prediction of land parcel changes influenced by BRT-related factors with statistical rigor. Additionally,

(Please see page # 15, lines 505-508 of manuscript)

Benchmarking in the initial comparative analysis complemented the decision model by establishing a baseline for understanding land changes prior to BRT implementation, further validating the predictive capabilities of the C5.0 model.

(Please see page # 15, lines 513-515 of manuscript)

• Define the term "cross-validation techniques".

 

Reply:  Thank you for pointing that out. In response to this comment, we have replaced cross-validation techniques with model enhancement strategies to better reflect the approach used. Model enhancement strategies include methods like boosting and pruning, which improve the model's accuracy and performance. Cross-validation, by comparison, involves partitioning the dataset to validate model performance across multiple subsets, ensuring reliability across varied data. This adjustment clarifies the steps taken to strengthen the model's predictive capabilities.

“Model enhancement strategies”

(Please see page # 6, lines 267 of manuscript)

 

 

• When you mention using the C5.0 model, it would be helpful to add a brief explanation of how that model works and what specific aspects it analyzes.

Reply:  Thank you for the suggestion. A brief explanation of the C5.0 model has been added to clarify its functionality. The C5.0 decision tree algorithm works by splitting the dataset based on variables that provide the highest information gain, effectively prioritizing features that most influence the outcome. Specifically, in this study, the model examines variables such as land value, proximity to BRT stations, building conditions, and population density, analyzing how each contributes to changes in land parcel dynamics. This approach provides a structured, step-by-step analysis of how BRT-related factors impact urban development, making it well-suited to understanding complex, multi-variable relationships.

“The C5.0 model operates by segmenting data into smaller, manageable subsets, using recursive partitioning to develop a decision tree that identifies key predictive variables, optimizing classification accuracy in a variety of contexts.”

(Please see page # 15, lines 525-528 of manuscript)

• In the research design section, you can add a sentence that explains how the results of the analysis will be presented or interpreted.

Reply:  Thank you for the suggestion. A sentence has been added in the research design section to clarify the presentation and interpretation of the analysis results. Specifically, the results will be presented by visually illustrating the decision tree outcomes, showing the influence of BRT-related factors on land parcel changes.

“The final outcome was presented by visually illustrating the decision tree outcomes interpreted by analyzing the significance of each variable in relation to land parcel changes influenced by BRT factors “

(Please see page # 7, lines 288-290 of manuscript)

• In the data analysis section, consider breaking long sentences into shorter, clearer sentences. In some parts you use different expressions for the same concepts. It would be a good idea to pick one term and use it consistently.

Reply:  Thank you for the suggestion. The data analysis section has been revised to improve clarity by breaking up longer sentences into shorter, more concise statements. Additionally, terminology has been standardized throughout to ensure consistent use of expressions for key concepts. This enhances readability and ensures that the section is both clear and cohesive.

• when building the model, add more explanation for the steps used in the C5.0 algorithm

Reply: Thank you for the suggestion. I have added a detailed explanation of each step involved in the C5.0 algorithm within the model-building section. This includes the data splitting process, calculation of information gain for attribute selection, recursive tree construction, pruning to prevent overfitting, and the final tree structure for classification. This additional detail provides a clearer understanding of how the C5.0 algorithm functions and how it effectively contributes to the analysis.

“o        Splitting: The C5.0 algorithm initially selected the attribute with the highest information gain, like land value in 2010 or proximity to the BRT station, as the root node. It continued to split based on other significant factors, building out the branches.

o          Recursive Tree Construction: Each node represented a decision criterion (e.g., parcels within 500 meters of the BRT station), with the tree expanding until a set stopping criterion, such as no further classification improvement.

o          Resultant Decision Tree: Nodes in the tree illustrated decisions based on the attributes, while leaves represented final classifications, like predicting higher land value for parcels near the BRT with good accessibility and high passenger volume.”

(Please see page # 16-17, lines 592-603 of manuscript)

• Correct the form of the tables according to MDPI requirements. The letters in the tables should be smaller than in the text

Reply:  Thank you for the feedback. I have adjusted the tables to align with MDPI formatting guidelines. The font size within the tables has been reduced to ensure it is smaller than the main text, as required.

• The first figure between lines 176 and 177 should be clearly visible, so that the letters are in the size of the text. The text in the second image between lines 176 and 177 is completely invisible. Correct it

Reply:   Thank you for the suggestion. I have updated the first figure to ensure that the text is clearly visible and matches the size of the main text. Additionally, the second image has been corrected so that all text within it is now clearly legible.

 

• Page 21 is very bad. It can't be like that in an article. Sort it out

Reply: Thank you for this valuable feedback. In response to your valuable comment; the C5.0 model have been re-drawn at figure # 12 and its details have been prepared in tabular form in table # 13. 

(Please see page # 24-28, lines 826-831 of manuscript)

• The research is quite thorough and uses an interesting approach to analysis. Something that could be considered is additional research into community impacts, such as social and economic dynamics, given the changes brought about by the new routes. Also, consideration of potential negative effects, such as increased pollution, would further enrich the analysis.

Reply:   Thank you for the insightful comment. I acknowledge the potential for broader community impacts, including social and economic dynamics, resulting from the BRT system’s implementation. While this study primarily focuses on land parcel changes, I agree that future research could explore these additional dimensions to provide a more comprehensive view of the BRT's effects. The potential negative effects, such as pollution, can indeed be mitigated by promoting further research and implementing the outcomes effectively. Moreover, the insights from this research open avenues for future studies focusing on economic, social, and environmental impacts, which are essential for projecting sustainable, holistic urban development. This model can serve as an architecture for integrating AI-based and data science solutions, leveraging our findings to enhance sustainable development in urban transit planning - a focus we are exploring further in our next research paper. This approach aims to advance data-driven decision-making, improve transit system efficiency, and support sustainable urban growth strategies.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The revisions are well done overall, but Figures 13 and 14 are low-quality and appear pixelated; it is recommended to redraw them for improved clarity and consistency.

Author Response

The revisions are well done overall, but Figures 13 and 14 are low-quality and appear pixelated; it is recommended to redraw them for improved clarity and consistency.

Reply : Thank you for the feedback. Figures 13 and 14 have been redrawn with enhanced resolution and clarity to ensure consistency with the quality of other visuals in the manuscript. This update aims to improve readability and maintain the presentation standards expected for publication.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Correct Figures 2, 13, and 14 to make the text visible

Change all tables to MDPI standard

Author Response

3.1 Correct Figures 2, 13, and 14 to make the text visible

Reply: Thank you for the suggestion. Figures 2, 13, and 14 have been updated to ensure that all text is clearly visible and aligned with the readability standards for publication. This adjustment enhances the figures’ clarity and consistency throughout the manuscript.

3.2 Change all tables to MDPI standard

Reply: Thank you for the feedback All tables have been adjusted to conform to the MDPI standard, including font size, layout, and alignment, ensuring consistency and readability throughout the manuscript.

Author Response File: Author Response.pdf